Cancer is the attributed cause of death in one in four cases in the United States and metastasis, a complex multistep process leading to the spread of tumors, is responsible for more than 90% of these deaths. However, predicting the location of these secondary tumor sites is still an elusive goal. One of the fundamental hurdles is to understand the trajectory of cell movement through the vascular system and the likelihood of penetration of the vessel wall. Studies have demonstrated that more than 50% of cancer metastatic sites could be explained by the blood flow pattern between the primary and secondary; however, the development of predictive models is still needed. Insight into the underlying mechanisms of cancer metastasis will provide insight into disease progression and lead to the development of new diagnostic or therapeutic methods targeting regions of the vasculature likely to incur secondary tumor sites. Tools that can be easily tuned to allow not only patient-specific but cell-specific modeling would complement ongoing in vitro experiments and provide this critical insight. Such computational models would allow researchers to probe the influence of different biophysical properties on cancer-specific cell behavior without the need for either expensive experimental trials for each cell-type or extrapolate from findings for one cancer to apply to another. An expected outcome of this research to create a usable, scalable, and extensible software framework for use by the wider biomedical research community to study the role of biophysical properties on a cell's transport and potential arrest. On such a platform, users will be able to introduce models of their cells-of-interest and perform simulations on them with models we (or others in the community) have developed. The ability to seamlessly introduce new cell-types with minimal effort will foster entirely new collaborations between researchers and provide biologists who would not traditionally leverage computational resources to study cell-specific properties in the context of realistic vascular geometries. This work will set the stage for future studies expanding the capabilities of this open source model.